Unsupervised Domain Adaptation (UDA) methods facilitate knowledge transfer
from a labeled source domain to an unlabeled target domain, navigating the
obstacle of domain shift. While Convolutional Neural Networks (CNNs) are a
staple in UDA, the rise of Vision Transformers (ViTs) provides new avenues for
domain generalization. This paper presents an innovative method to bolster ViT
performance in source-free target adaptation, beginning with an evaluation of
how key, query, and value elements affect ViT outcomes. Experiments indicate
that altering the key component has negligible effects on Transformer
performance. Leveraging this discovery, we introduce Domain Representation
Images (DRIs), feeding embeddings through the key element. DRIs act as
domain-specific markers, effortlessly merging with the training regimen. To
assess our method, we perform target adaptation tests on the Cross Instance DRI
source-only (SO) control. We measure the efficacy of target adaptation with and
without DRIs, against existing benchmarks like SHOT-B* and adaptations via
CDTrans. Findings demonstrate that excluding DRIs offers limited gains over
SHOT-B*, while their inclusion in the key segment boosts average precision
promoting superior domain generalization. This research underscores the vital
role of DRIs in enhancing ViT efficiency in UDA scenarios, setting a precedent
for further domain adaptation explorations.

该研究以无监督域自适应为主题，探索了如何利用关键元素来强化 Vision Transformers 在无源目标适应中的性能，通过引入域表示图像（DRIs）作为关键组件，提高了 Transformer 在领域泛化中的效率。